Neural Networks for Predicting Options Volatility Mary Malliaris and Linda Salchenberger Loyola University Chicago World Congress on Neural Networks San Diego 1994

Introduction Volatility is a measure of price movement used to measure risk Traders use two estimates of options volatility – Historical – Implied We will compare these with a neural network model for predicting options volatility

Historical and Implied Historical – The annualized standard deviation of n-1 rates of daily return Implied – The volatility calculated using the Black-Scholes model

Neural Network Backpropagation model Frequently applied to prediction problems in nonlinear cases Used to forecast volatility one day ahead

Data S&P 100 (OEX) Daily closing call and put prices and the associated exercise prices closest to at-the- money S&P 100 Index prices Call volume and put volume Call open interest and put open interest All of 1992

Volatilities Historical – Three estimates using Index price samples of sizes 30, 45, and 60 Implied – Black-Scholes model calculations for the closest at-the-money call for three contracts: those expiring in the current month, one month away, and two months away (nearby, middle, and distant)

Historical vs Implied Dates of ForecastMADMSECorrect Directions Jun 22 – Jul Jul 20 – Aug Aug 24 – Sep Sep 21 – Oct Oct 19 – Nov Nov 23 – Dec Dec 21 – Dec

Network vs Implied Dates of ForecastMADMSECorrect Directions Jun 22 – Jul Jul 20 – Aug Aug 24 – Sep Sep 21 – Oct Oct 19 – Nov Nov 23 – Dec Dec 21 – Dec

Discussion The neural network model uses both short term historical data and contemporaneous variables to forecast future implied volatility NN predictions can be made for a full trading cycle The network forecasts were more accurate estimates of volatility